A power system ultra-low frequency oscillation risk analysis method, device and medium

Abstract

The application discloses a kind of power system ultra-low frequency oscillation risk analysis method, equipment and medium, including recording the contact strength of water and electricity mainly sending end power grid and receiving end power grid, and into equivalent external reactance of synchronous generator;Based on small signal analysis theory, simultaneously synchronous machine model, excitation system model and speed regulator model are constructed small signal analysis model;Based on eigenvalue analysis theory, small signal model is analyzed, and ultra-low frequency oscillation risk analysis index is obtained.The oscillation mode of power system can be calculated by eigenvalue, and the ultra-low frequency oscillation risk analysis index is established.The ultra-low frequency oscillation risk analysis index is a risk analysis index considering the contact strength of power system, which can better reflect the actual power system ultra-low frequency oscillation risk, and solves the problem that the risk analysis for ultra-low frequency oscillation does not consider the influence of power system connection strength on ultra-low frequency damping oscillation, making the risk analysis more close to the actual power grid situation.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method, equipment, and medium for analyzing the risk of ultra-low frequency oscillations in power systems. Background Technology

[0002] Ultra-low frequency oscillations are a new type of stability problem that has emerged in power systems in recent years, which is different from traditional low-frequency oscillations. They mostly occur in power grids dominated by hydropower. This problem has been observed many times in actual power grid operation tests in China, and its oscillation characteristics are different from those of low-frequency oscillations.

[0003] Most of the low-frequency oscillations currently studied are in the range of 0.1-2.5Hz. This is mainly due to the negative damping effect of the system with the generator and excitation system, and is mostly a relative oscillation between rotors, which belongs to the power angle stability problem.

[0004] Ultra-low frequency oscillation has three prominent characteristics: first, the oscillation frequency is below 0.1Hz, which is outside the range of low frequency oscillation; second, the characteristics of ultra-low frequency oscillation are manifested in the frequency oscillation of the system, which is strongly correlated with the prime mover and speed governor of the system; and third, the frequencies of each point in the system basically maintain the same oscillation, without relative swaying between generator rotors, hence the name ultra-low frequency oscillation.

[0005] In addition, low-frequency oscillations often occur during grid interconnection, particularly on weakly connected, long-distance, heavily loaded transmission lines, under conditions of using fast, high-amplification excitation systems. Ultra-low-frequency oscillations, on the other hand, are more common in recent years with large-scale grids asynchronously interconnected or operating in islanded configurations via high-voltage direct current systems. Both types of oscillations share a common characteristic: they are caused by insufficient or even negative damping of the system.

[0006] Existing technologies for risk analysis of ultra-low frequency oscillations all focus on the governor in a single machine system, analyzing the impact of the turbine governor on the damping of ultra-low frequency oscillations. Currently, there is no analysis or analysis method that considers the impact of power system connection strength on the risk of ultra-low frequency oscillations in engineering scenarios. Summary of the Invention

[0007] The technical problem to be solved by this application is that the existing technology for risk analysis of ultra-low frequency oscillations does not consider the influence of power system connection strength on ultra-low frequency damped oscillations, making the risk analysis not close enough to the actual power grid situation. The purpose is to provide a method, equipment and medium for risk analysis of ultra-low frequency oscillations in power systems, which solves the problem that the risk analysis for ultra-low frequency oscillations does not consider the influence of power system connection strength on ultra-low frequency damped oscillations, making the risk analysis not close enough to the actual power grid situation.

[0008] This invention is achieved through the following technical solution:

[0009] The first aspect of this invention provides a method for risk analysis of ultra-low frequency oscillations in power systems, including...

[0010] Record the connection strength between the sending-end power grid and the receiving-end power grid, which is mainly hydropower, and include the connection strength in the equivalent external reactance of the synchronous generator;

[0011] Extract synchronous generator parameters to establish a synchronous machine model;

[0012] Extract excitation system parameters and establish an excitation system model;

[0013] Extract parameters of the turbine governor and establish a governor model;

[0014] Based on the small-signal analysis theory, a small-signal analysis model is constructed by combining the synchronous machine model, the excitation system model, and the speed governor model.

[0015] Based on the eigenvalue analysis theory, the small-signal model is analyzed to obtain the risk analysis index for ultra-low frequency oscillation.

[0016] In the aforementioned technical solutions, the connection strength between the sending-end and receiving-end power grids affects the risk analysis of ultra-low frequency oscillations (ULF). However, existing technologies, particularly the traditional synchronous machine models used for ULF risk analysis, do not consider this connection strength, resulting in risk analysis that is not closely aligned with actual power grid conditions. In this application's technical solution, the connection strength between the sending-end and receiving-end power grids (primarily hydropower) is recorded and incorporated into the equivalent external reactance of the synchronous generator. This connection strength is included as an influencing factor in the risk analysis, making the risk analysis more closely reflective of actual power grid conditions.

[0017] The parameters of synchronous generators, excitation systems, and turbine governors that influence the risk analysis of ultra-low frequency oscillations in hydropower-dominated power systems are extracted. Based on these parameters and state variables, synchronous machine models, excitation system models, and governor models are established to simulate the operation of the power system.

[0018] A small-signal analysis model is constructed using small-signal analysis theory, and eigenvalue analysis is then used to analyze the model to obtain eigenvalues. These eigenvalues ​​can be used to calculate the oscillation modes of the power system and establish an ultra-low frequency (ULF) oscillation risk analysis index. This ULF oscillation risk analysis index considers the power system interconnection strength, thus better reflecting the actual ULF oscillation risk of the power system. It solves the problem that risk analysis for ULF oscillations did not consider the influence of power system interconnection strength on ULF damped oscillations, making the risk analysis less closely reflective of actual power grid conditions.

[0019] In one alternative embodiment, the synchronization machine model is as follows:

[0020] x1=x s +x L (19)

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] In the above formula, Δ represents the increment of each variable, the superscript "·" indicates the state variable of the corresponding variable, and among the variables, δ is the generator power angle, ω is the generator speed, and M... m and M e E' represents the mechanical torque and electromagnetic torque of the generator. q and E” q These are the q-axis transient potential and subtransient potential, E, respectively. f For the magnetizing potential, i d and i q E” represents the d-axis and q-axis components of the generator current. d Let x' be the d-axis subtransient potential. d and x” d Let ω0 be the transient and subtransient reactances, and T be the rated speed of the generator. J Let T' be the generator inertial time constant and the d-axis transient time constant. d0 and the subtransient time constant T” d0 x d Let x be the d-axis reactance and the q-axis reactance. q and subtransient reactance x” q ,T” q0 Let x1 be the subtransient time constant, x1 be the generator external reactance, and x be the external reactance. s For generator stator reactance; x L This refers to the line reactance.

[0027] In one alternative embodiment, the excitation system model is as follows:

[0028]

[0029]

[0030]

[0031] In the above formula, K A and T A K represents the gain and time constant of the voltage regulator, respectively.E and T E These represent the gain and time constant of the excitation regulation, respectively, K. F and T F These are the proportional gain and time constant of the feedback loop, respectively.

[0032] In one alternative embodiment, the speed governor model is as follows:

[0033]

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] In the above formula, s is the differential operator, and P GV K is the gate opening output by the speed governor control system. P1 K d1 K i1 These represent the proportional and derivative gain and integral time constant of the speed control system, respectively. P T is the adjustment coefficient. d1 K is the time constant of the integrator; P K d T i These represent the proportional and differential gain of the hydraulic circuit and the integral time constant, respectively, T. f T is the time constant of the inertial element. w ΔG1, ΔG2, ΔG3, and ΔG4 are the time constants for the water hammer effect; ΔG1, ΔG2, ΔG3, and ΔG4 are the outputs of each integral and differential component.

[0041] In one alternative embodiment, the small-signal analysis model is as follows:

[0042]

[0043] In the above formula: Δx is a matrix containing all state variables, and A is a coefficient matrix that reflects the relationship between relevant variables and state variables after combining equations (1) to (16).

[0044] In one optional embodiment, the method for analyzing the small-signal model based on eigenvalue analysis theory is as follows:

[0045] λI-A=0 (36)

[0046] In the above formula, λ is the eigenvalue, I is an n×n matrix with diagonal elements of 1 and other elements of zero, and n is the number of state variables in formula (17).

[0047] In one optional embodiment, the risk analysis index for ultra-low frequency oscillations is:

[0048] In one alternative embodiment, the external reactance of the synchronous generator in the synchronous machine model includes the generator stator reactance x. s and line reactance.

[0049] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for risk analysis of ultra-low frequency oscillations in a power system.

[0050] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for risk analysis of ultra-low frequency oscillations in a power system.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] This invention analyzes the actual operating scenarios of ultra-low frequency oscillations currently occurring in power grids. The proposed ultra-low frequency oscillation risk analysis method, which considers power grid strength, is more consistent with the actual power grid situation. The proposed ultra-low frequency oscillation evaluation index comprehensively considers the influence of the generator itself, the prime mover speed governor, and the degree of grid connection on the grid oscillation mode, and can more accurately evaluate and analyze the risk and stability of the system generating ultra-low frequency oscillations.

[0053] Compared to risk analysis of ultra-low frequency oscillations that does not consider the strength of power system connections, risk analysis of ultra-low frequency oscillations that does consider the strength of power system connections has the following advantages:

[0054] 1. Considering the actual occurrence scenarios of ultra-low frequency oscillations, the system interconnection strength is included as a key factor affecting ultra-low frequency oscillations, and an ultra-low frequency oscillation analysis model applicable to different interconnection strengths of the power grid is established;

[0055] 2. Taking into account the connection strength of the prime mover and governor, generator and power grid, the oscillation mode of the system is determined through eigenvalue analysis, and an ultra-low frequency oscillation risk analysis method applicable to different power grid connection strengths is proposed.

[0056] 3. It is more suitable for the actual occurrence scenarios of ultra-low frequency oscillation problems and is more accurate in practical applications;

[0057] 4. The risk analysis method proposed in this invention not only considers the internal fundamental factors that generate ultra-low frequency oscillations, but also the external inducing factors;

[0058] 5. The risk analysis method proposed in this invention can accurately identify the risk of ultra-low frequency oscillation of the system when the system connection strength changes. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0060] Figure 1 This is a schematic diagram of the structure of a hydropower-based power grid provided in one embodiment of this application;

[0061] Figure 2 This is a schematic diagram of the DC1A type excitation structure provided in an embodiment of this application;

[0062] Figure 3 This is a schematic diagram of the structure of a water turbine governor provided in one embodiment of this application;

[0063] Figure 4 This is a schematic diagram of the structure of a hydraulic regulating mechanism and prime mover model of a water turbine provided in an embodiment of this application;

[0064] Figure 5 X provided in one embodiment of this application L Simulation results for a grid interconnection strength of 0.1H;

[0065] Figure 6 X provided in one embodiment of this application L Simulation results under a 10H power grid interconnection strength;

[0066] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0068] Example 1

[0069] Embodiment 1 of the present invention provides a method for risk analysis of ultra-low frequency oscillations in power systems, the method comprising the following steps:

[0070] S1. Record the connection strength between the sending-end power grid (mainly hydropower) and the receiving-end power grid, and include the connection strength in the equivalent external reactance of the generator.

[0071] Figure 1 This is a schematic diagram of the main power grid structure provided in this embodiment, such as... Figure 1 As shown, the main hydropower power grid comprises two parts: the sending-end grid and the receiving-end grid. Between these two grids lies a connection strength that influences the risk analysis of ultra-low frequency oscillations. This connection strength can be expressed as the reactance of the transmission line multiplied by the voltage. L express.

[0072] Existing technologies, particularly the traditional synchronous machine models used in ultra-low frequency oscillation risk analysis, do not include connection strength, resulting in risk analysis that does not closely reflect actual power grid conditions. In this embodiment, the connection strength between the sending-end and receiving-end power grids (primarily hydropower) is recorded and incorporated into the equivalent external reactance of the generators in the traditional synchronous machine model. By including the connection strength between the sending-end and receiving-end power grids in the influencing factors during risk analysis, the risk analysis becomes more closely aligned with actual power grid conditions.

[0073] S2. Extract the synchronous generator parameters and establish a synchronous machine model based on the synchronous generator parameters and various variables during generator operation.

[0074] The synchronous generator parameters include the generator's rated speed ω0 and the generator's inertial time constant T. J d-axis transient time constant T' d0 and the subtransient time constant T” d0 d-axis reactance x d q-axis reactance x q and subtransient reactance x” q Subtransient time constant T” q0 , Generator external reactance x1.

[0075] Unlike the external reactance of conventional generators in the prior art, the external reactance of the generator in this embodiment records the grid interconnection strength. Therefore, the external reactance of the generator in this embodiment includes the generator stator reactance x. s It also includes line reactance.

[0076] The variables during generator operation include the generator power angle δ, the generator speed ω, and the generator mechanical torque M. m Electromagnetic torque M e q-axis transient potential E' q and subtransient potential E”q Magnet potential E f d-axis component i of generator current d and q-axis component i q d-axis subtransient potential E” d .

[0077] A synchronous machine model is established by combining the synchronous generator parameters and variables during generator operation. The synchronous machine model is as follows:

[0078] x1=x s +x L (37)

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] In the above formula, Δ represents the increment of each variable, and the superscript "·" indicates the state variable of the corresponding variable.

[0085] The state variables in the synchronous machine model, including the generator power angle, can be obtained through the synchronous machine model derived from equations (1) to (6). State variables of generator speed q-axis transient potential state variable The state variables of the q-axis subtransient potential The state variables of the d-axis subtransient potential

[0086] S3. Extract the excitation system parameters and convert the output U of the voltage regulator. R The output U of the feedback loop F and the output E of the excitation regulation fd The excitation system model is established using these as state variables.

[0087] Figure 2 This is a schematic diagram of the DC1A type excitation structure provided in this embodiment, as shown below. Figure 2 As shown, the excitation system is a general term for the power supply that supplies the excitation current to the synchronous generator and its auxiliary equipment. The DC1A type excitation structure includes two main parts: the excitation power unit and the excitation regulator. The excitation power unit provides the excitation current to the synchronous generator rotor, and the excitation regulator controls the output of the excitation power unit according to the input signal and the given regulation criteria.

[0088] Extract the excitation system parameters of the DC1A type excitation structure. The excitation system parameters include the voltage reference value U. ref The gain K of the voltage regulator A and time constant T A The gain K of excitation regulation E and time constant T E The proportional gain K of the feedback loop F and time constant T F .

[0089] The output U of the voltage regulator in the DC1A type excitation structure R The output U of the feedback loop F and the output E of the excitation regulation fd As state variables, an excitation system model is established by combining the excitation system parameters. The excitation system model is as follows:

[0090]

[0091]

[0092]

[0093] S4. Extract the parameters of the turbine governor and select the outputs of the integral and differential components of the turbine governor as state variables to establish the governor model.

[0094] Figure 3 This is a schematic diagram of the turbine governor provided in this embodiment. Figure 4 This is a schematic diagram of the hydraulic regulating mechanism of the turbine and the prime mover model provided in this embodiment. The turbine governor is one of the most important auxiliary control devices of the turbine-generator unit. The turbine adjusts its guide vane mechanism according to the speed deviation signal, maintaining the balance between the power and combined power of the turbine-generator unit. The turbine is also an important factor in the risk analysis of ultra-low frequency oscillations in power systems. The turbine includes a speed regulating component and a hydraulic component, wherein the speed regulating component is as follows... Figure 3 As shown, the hydraulic components are as follows: Figure 4 As shown.

[0095] Extracting parameters from the turbine governor, including the speed regulation system ratio K in the speed regulation stage. P1 The gain K of the differential element d1 Integral time constant K i1 b P The adjustment coefficient b P T d1 The time constant T of the integrator d1 And the proportion K of the hydraulic components. P and the gain K of the differential elementD and the integral time constant T i T f The time constant T of the inertial element f T w The time constant T for water hammer effect w .

[0096] The outputs ΔG1, ΔG2, ΔG3, and ΔG4 of each integral and derivative stage are selected as state variables to establish the governor model, which is as follows:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] In the above formula, s is the differential operator, and P GV This refers to the gate opening output by the speed controller system.

[0105] S5. Based on the small-signal analysis theory, a small-signal analysis model is constructed by combining the synchronous machine model, the excitation system model, and the speed governor model.

[0106] Based on the small-signal analysis theory, and combining equations (1) to (16), a small-signal analysis model for a hydropower-based power grid can be obtained.

[0107] This small-signal analysis model includes the synchronous generator parameters, generator operation variables, and state variables of the generator operation variables in the synchronous machine model; and the excitation system parameters, excitation system state variables, and voltage regulator output U in the excitation system model. R The output U of the feedback loop F and the output E of the excitation regulation fd The parameters of the speed regulating element, the parameters of the hydraulic element, and the output variables of each integral and differential element as state variables in the speed governor model.

[0108] A small-signal analysis model is established by combining reaction variables and state variables.

[0109] Furthermore, the small signal analysis model can be written as follows: The form is given by , where Δx is a matrix containing all reaction-related variables, and A is a coefficient matrix representing the relationship between reaction-related variables and state variables after solving all equations simultaneously. It is a matrix containing all state variables.

[0110] The coefficient matrix A, representing the relationship between relevant variables and state variables, can be obtained by combining all equations using a small-signal analysis model.

[0111] S6. Based on the eigenvalue analysis theory, the small signal model is analyzed to obtain the risk analysis index of ultra-low frequency oscillation after considering the system connection strength.

[0112] Specifically, the small signal analysis model obtained through step S5 includes a coefficient matrix reflecting the relationship between relevant variables and state variables after all equations are solved simultaneously. The eigenvalue calculation equation λI-A=0 is constructed, and the eigenvalues ​​are calculated on the coefficient matrix, where λ is the eigenvalue, I is an n×n matrix with diagonal elements of 1 and other elements of zero, and n is the number of state variables in equation (17).

[0113] The above steps can yield the eigenvalues ​​of the coefficient matrix that reflects the relationship between relevant variables and state variables. These eigenvalues ​​can be used to analyze the risk of ultra-low frequency oscillations in the power system.

[0114] Eigenvalue analysis, also known as mode analysis, is a method for studying the stability of power systems under small disturbances. Essentially, it linearizes nonlinear equations and solves the state matrix to obtain specific oscillation modes. The theoretical basis of this method is the Lyapunov linearization method, which is widely used to evaluate the stability of nonlinear systems. Based on the state matrix, eigenvalues ​​and eigenvectors are obtained. When the system has eigenvalues ​​in the positive plane, the system is at risk of instability. The real and imaginary parts of the conjugate eigenvalues ​​reflect the decay rate of the oscillation amplitude and the oscillation frequency of the oscillation mode, respectively. The oscillation modes of the system can be calculated from the eigenvalues.

[0115] In this process, the eigenvalues ​​of the coefficient matrix reflecting the relationship between relevant and state variables are substituted into a hydropower-dominated simulation model for calculation, yielding the oscillation frequency corresponding to the eigenvalues ​​under a certain grid interconnection strength. The oscillation frequency is then assessed to determine if there is a risk of ultra-low frequency oscillation. A critical value for this risk is identified, and the model is divided based on this value: values ​​exceeding the critical value indicate no risk of ultra-low frequency oscillation, while values ​​below the critical value indicate a risk of ultra-low frequency oscillation.

[0116] In this embodiment of the application, after analyzing the risk of ultra-low frequency oscillations in the power system through steps S1 to S7, the ultra-low frequency oscillation risk analysis index after considering the system interconnection strength is as follows:

[0117]

[0118] Figure 5 and Figure 6 They are X L Simulation results and X under a grid interconnection strength of 0.1H. L Simulation results diagram under a 10H power grid interconnection strength.

[0119] like Figure 5 As shown, X L When the frequency is 0.1H, the system does not have the risk of ultra-low frequency oscillation. At this point, both the traditional analysis model and the model proposed in this invention can effectively identify the oscillation frequency of the system and analyze the risk of ultra-low frequency oscillation. Figure 6 As shown, the system connection strength changes to X. L When the value of H is 10 and other parameters remain unchanged, the simulation results show that the system has the risk of ultra-low frequency oscillation. However, the traditional analysis model cannot accurately identify the oscillation frequency and ultra-low frequency oscillation risk of the system. The analysis results of the oscillation frequency and ultra-low frequency oscillation risk identified by the analysis model proposed in this invention are consistent with the simulation results, which proves that the analysis model and risk analysis method proposed in this invention are effective and can be applied to the analysis of the ultra-low frequency oscillation risk of the system when the connection strength of the hydropower-based power grid changes.

[0120] In X L =0.1H grid interconnection strength and X L The following table compares the simulation analysis results of the traditional analysis model and the analysis model provided in the embodiments of this application under a power grid interconnection strength of 10H:

[0121]

[0122]

[0123] The table above shows that in X L At a connection strength of 10H, the traditional analysis model for analyzing the risk of ultra-low frequency oscillations shows no risk; however, the analysis model provided in this embodiment shows a risk of ultra-low frequency oscillations. Simulation analysis reveals that at X... L Power systems with a connection strength of 10H are at risk of ultra-low frequency oscillations. Therefore, the analysis method and model provided in this embodiment are closer to the power systems in actual applications and can more accurately analyze the risk of ultra-low frequency oscillations in power systems.

[0124] Compared to risk analysis of ultra-low frequency oscillations that does not consider the strength of power system connections, the risk analysis of ultra-low frequency oscillations that takes into account the strength of power system connections, based on the above technical solution, has the following advantages:

[0125] 1. Considering the actual occurrence scenarios of ultra-low frequency oscillations, the system interconnection strength is included as a key factor affecting ultra-low frequency oscillations, and an ultra-low frequency oscillation analysis model applicable to different interconnection strengths of the power grid is established;

[0126] 2. Taking into account the connection strength of the prime mover and governor, generator and power grid, the oscillation mode of the system is determined through eigenvalue analysis, and an ultra-low frequency oscillation risk analysis method applicable to different power grid connection strengths is proposed.

[0127] 3. It is more suitable for the actual occurrence scenarios of ultra-low frequency oscillation problems and is more accurate in practical applications;

[0128] 4. The risk analysis method proposed in this invention not only considers the internal fundamental factors that generate ultra-low frequency oscillations, but also the external inducing factors;

[0129] 5. The risk analysis method proposed in this invention can accurately identify the risk of ultra-low frequency oscillation of the system when the system connection strength changes.

[0130] Example 2

[0131] Figure 7 This is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of the present invention, as shown below. Figure 7 As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the computer device can be one or more. Figure 7 Taking a processor 21 as an example; the processor 21, memory 22, input device 23, and output device 24 in an electronic device can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0132] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, thereby implementing the power system ultra-low frequency oscillation risk analysis method of Embodiment 1.

[0133] The memory 22 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 22 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 22 may further include memory remotely located relative to the processor 21, which can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0134] Input device 23 can be used to receive user input such as ID and password. Output device 24 is used to output the network configuration page.

[0135] Example 3

[0136] Embodiment 3 of the present invention also provides a computer-readable storage medium, wherein the computer-executable instructions, when executed by a computer processor, are used to implement a power system ultra-low frequency oscillation risk analysis method as provided in Embodiment 1.

[0137] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of power system ultra-low frequency oscillation risk analysis, characterized in that, The method comprises the following steps: Record the contact strength of the water and electricity dominated sending end power grid and the receiving end power grid, and count the contact strength into the equivalent external reactance of the synchronous generator; Extract the parameters of the synchronous generator to establish a synchronous generator model, extract the parameters of the excitation system to establish an excitation system model, and extract the parameters of the water turbine governor to establish a governor model; Based on the small signal analysis theory, the small signal analysis model is constructed by simultaneously solving the synchronous generator model, the excitation system model and the governor model; Based on the eigenvalue analysis theory, the small signal model is analyzed to obtain the ultra-low frequency oscillation risk analysis index; The synchronous generator model is: In the above equations, Δ denotes the increment of each variable, the superscript "•" denotes the state variable of the corresponding variable, and δ in the variable is the generator power angle, ω is the generator speed, M m and M e are the mechanical torque and electromagnetic torque of the generator, E ' q and E '' q are the q-axis transient and sub-transient electric potentials, E f is the field voltage, i d and i q are the d-axis and q-axis components of the generator current, E '' d is the d-axis sub-transient electric potential, x ' d and x '' d are the transient and sub-transient reactances, ω0 is the rated speed of the generator, T J is the inertia time constant of the generator, the d-axis transient time constant T ' d0 and the sub-transient time constant T '' d0 , x d is the d-axis reactance, the q-axis reactance x q and the sub-transient reactance x '' q , T '' q0 is the sub-transient time constant, x1 is the external reactance of the generator, x s is the stator reactance of the generator; is the line reactance; The excitation system model is: In the above equations, and are the gain and time constant of the voltage regulator, respectively, and are the gain and time constant of the field regulation, respectively, and are the proportional gain and time constant of the feedback loop, respectively; The governor model is: In the above equation, s is the differential operator, P GV is the gate opening of the water gate controlled by the governor control system, are the proportional and differential gains and the integral time constant of the governor system, respectively, is the correction coefficient, T d1 is the integral time constant; , , T i are the proportional and differential gains and the integral time constant of the hydraulic system, respectively, T f is the inertial time constant, T w is the water hammer time constant; are the outputs of the integral and differential elements, respectively. The small signal analysis model is: In the above formula: Δx is a matrix containing all state variables, is the coefficient matrix of the relationship between the reaction-related variables and the state variables after the simultaneous equations of formula (1) to formula (16).

2. The method of claim 1, wherein the method further comprises: The method for analyzing the small signal model based on the eigenvalue analysis theory is as follows: In the above formula, is an eigenvalue, is an n x n matrix with 1 on the diagonal and 0 elsewhere, n being the number of state variables in formula (17).

3. The method of claim 2, wherein the method further comprises: The ultra-low frequency oscillation risk analysis index is .

4. The method of claim 1, wherein the method further comprises: The synchronous generator external reactance in the synchronous machine model includes the generator stator reactance x s and the line reactance.

5. An electronic device, comprising: The computer program stored in the memory and executable on the processor, when the processor executes the program, realizes the power system ultra-low frequency oscillation risk analysis method as claimed in claims 1 to 4.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the power system ultra-low frequency oscillation risk analysis method as claimed in claims 1 to 4.

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